Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification

Jiachen Chen, Danyang Huang, Liyuan Wang, Kathryn L Lunetta, Debarghya Mukherjee, Huimin Cheng

Keywords: Transfer learning, Node Classification, Graph Convolution, High-Dimensional

Abstract: Node classification is a fundamental task, but obtaining node classification labels can be challenging and expensive in many real-world scenarios. Transfer learning has emerged as a promising solution to address this challenge by leveraging knowledge from source domains to enhance learning in a target domain. Existing transfer learning methods for node classification primarily focus on integrating Graph Convolutional Networks (GCNs) with various transfer learning techniques. While these approaches have shown promising results, they often suffer from a lack of theoretical guarantees, restrictive conditions, and high sensitivity to hyperparameter choices. To overcome these limitations, we employ a Graph Convolutional Multinomial Logistic Lasso Regression (GCR) model which simplifies GCN, and develop a transfer learning method called Trans-GCR based on the GCR model. We provide theoretical guarantees of the estimate obtained under the GCR model in high-dimensional settings. Moreover, Trans-GCR demonstrates superior empirical performance, has a low computational cost, and requires fewer hyperparameters than existing methods.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 7808